Digital Twins for Precision Pharmacology: An Integrative Review of Mechanistic Modeling, Virtual Patients, Pharmacokinetic Simulation, and Personalized Therapeutics

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Digital twins are emerging as computational representations that may support precision pharmacology by linking individual patient information with mechanistic models, virtual patient simulations, and longitudinal prediction. This integrative review examines how digital twin concepts intersect with systems pharmacology, physiologically based pharmacokinetic modeling, population pharmacokinetics, pharmacokinetic–pharmacodynamic integration, virtual populations, therapeutic drug monitoring, and personalized therapeutic decision support. The review integrates conceptual, methodological, and applied evidence according to model function, patient specificity, data requirements, updating capability, validation status, uncertainty handling, and intended clinical context. Mechanistic disease models and quantitative systems pharmacology can provide causal structures for representing biological and drug-response processes, whereas physiologically based and population pharmacokinetic models support individualized exposure prediction under explicit assumptions. Virtual patients and virtual populations enable scenario testing and heterogeneity analysis but should not be treated as equivalent to observed patients or clinical populations. Patient-specific simulation may assist therapeutic hypothesis generation, exposure interpretation, response assessment, and safety-oriented reasoning when models are appropriately calibrated and their uncertainty is communicated. However, pharmacokinetic simulation alone does not establish clinical benefit, dosing safety, or therapeutic effectiveness. Translation into personalized therapeutic decision support remains constrained by data quality, model identifiability, external and prospective validation, interoperability, workflow integration, governance, and human-factors requirements. Digital twins may therefore contribute to precision pharmacology only as bounded, evidence-qualified, and clinician-supervised systems. Responsible development requires transparent mechanistic assumptions, reliable patient-specific data, uncertainty-aware predictions, formal validation, auditability, and explicit decision boundaries.